Smart Grids in the Context of Smart Cities: A Literature Review and Gap Analysis

Abstract

Cities host over 50% of the world’s population and account for nearly 75% of the world’s energy consumption and 80% of the global greenhouse gas emissions. Consequently, ensuring a smart way to organize cities is paramount for the quality of life and efficiency of resource use, with emphasis on the use and management of energy, under the context of the energy trilemma, where the objectives of sustainability, security, and affordability need to be balanced. Electrification associated with the use of renewable energy generation is increasingly seen as the most efficient way to reduce the impact of energy use on GHG emissions and natural resource depletion. Electrification poses significant challenges to the development and management of the electrical infrastructure, requiring the deployment of Smart Grids, which emerge as a key development of Smart Cities. Our review targets the intersection between Smart Cities and Smart Grids. Several key components of a Smart City in the context of Smart Grids are reviewed, including elements such as metering, IoT, renewable energy sources and other distributed energy resources, grid monitoring, artificial intelligence, electric vehicles, or buildings. Case studies and pilots are reviewed, and metrics concerning existing deployments are identified. A portfolio of 16 solutions that may contribute to bringing Smart Grid solutions to the level of the city or urban settings is identified, as well as 11 gaps existing for effective and efficient deployment. We place these solutions in the context of the energy trilemma and of the Smart Grid Architecture Model. We posit that depending on the characteristics of the urban setting, including size, location, geography, a mix of economic activities, or topology, the most appropriate set of solutions can be identified, and an indicative roadmap can be built.

1. Introduction

It is projected that 66–68% of the world population will be urban by 2050 [1]. Also, 75–80% of world energy consumption is attributed to cities, which leads to the generation of 80% of the total greenhouse gas emissions [2,3,4]. Ensuring a smart way to organize cities is paramount for the quality of life and efficiency of resource use, with an emphasis on energy. This is expected to be done in the context of the energy trilemma, where the objectives of sustainability, security, and affordability need to be balanced [5].

In this context, electrification is increasingly seen as the most efficient way to simultaneously provide access to energy and reduce emissions impact. As society goes through the electrification of many of its processes, it challenges the electrical infrastructure, and Smart Grids emerge as a key development in Smart Cities. In this context, the concepts of Smart Cities and Smart Grids have raised increased attention and are analyzed in this paper.

Some authors consider Smart Cities as a subset of Smart Grids, while others consider Smart Grids as a subset of Smart Cities [3,4,6,7]. We consider that there is a zone of intersection between the two concepts, and it is in that intersection that this work is focused on.

This paper provides a literature review focused on aspects related to the integration of Smart Grids in the larger context of Smart Cities, namely on the integration of electric and energy systems. By referring to “smart grids in the context of smart cities”, we mean to address the aspects of Smart Cities that are related to energy, knowing that Smart Cities encompass several other dimensions.

This is an area where a review is insightful, as this is still an emergent area with many increasing publications per year, a fast pace of change, continuous experiments being performed, often presenting fragmented views, and already several real implementations with real data becoming available. The multiplicity of studies, pilots, and implementations can benefit from a structured view of how the intersection elements between Smart Grids and Smart Cities contribute to the energy trilemma challenges, fit the diverse layers that encompass an implementation (from communications infrastructure to business models) and address to the specific characteristics and features of Smart Cities.

The research performed has, therefore, the following objectives:

• Review the concepts of Smart City and Smart Grids, aiming to understand their main differences and their intersection points;
• Identify which components of Smart Grids are relevant for and present in the deployment of Smart Cities;
• Clarify how the relevant Smart Grids components for the deployment of Smart Cities relate to the energy trilemma challenges;
• Understand how the relevant Smart Grid components cluster around specific layers, from a communications layer to a business layer, to facilitate Smart Cities deployment;
• Understand which Smart Grid components address specific characteristics of a Smart City;
• Identify research gaps emerging from the previous analysis to assist in the effective and structured deployment of Smart Grids in Smart Cities.

Achieving these objectives provides fundamental structured insight on how to plan, develop, and evaluate the deployment of Smart Grids in the context of Smart Cities, besides stimulating policy insights and recommendations.

The innovative contributions of this paper include the following:

• Identification of 16 Smart Grid components that are relevant to in the context of Smart Cities deployment;
• Mapping of the 16 Smart Grid components into the different dimensions of the energy trilemma, identifying which ones contribute mostly to the Energy Security dimension, to the Energy Equity dimension, and to the Environmental Sustainability dimension;
• Mapping of the 16 Smart Grid components into distinct layers of a Smart Grid or Smart City model, namely Information and Communication Technologies (ICTs), Power Systems, markets and business layers;
• Mapping of the 16 Smart Grid components into the characteristics and features of a Smart City, providing insight on how the different functionalities and characteristics of a Smart City are being implemented based on Smart Grid components;
• 11 research gaps capturing the areas and dimensions that require further work and understanding for effective and efficient deployment of Smart Grids in the context of Smart Cities.

This review takes a narrative approach based on individual analysis of selected publications. The sources included IEEE, Elsevier, Wiley, Springer, and MDPI, making use of Google Scholar, ScienceDirect, and SCOPUS.

Circa 70 publications were selected, taking into consideration several criteria. On one side, there was the concern of having mostly recent references without disregarding original and older sources. Therefore, approximately 60% of the references are from the period 2020–2024, and approximately 8% are from the years before 2015. Furthermore, there was concern about adding institutional publications from diverse authorities and business views. This reinforces the view that besides an academic interest, there is also an associated societal interest. The diversity of geographies was also considered to avoid a possible bias. The number of citations was also taken into consideration. Figure 1 depicts the methodology for the literature review.

The organization of the paper is as follows. The concepts of Smart Cities and Smart Grids, which are fundamental for the development of the paper, are presented in Section 2, together with a discussion on their relationship and an attempt to bring the concepts together. The key components of a Smart City in the context of Smart Grids are discussed in Section 3. The emerging topics receive a more extensive analysis than elements that have been under analysis for a longer time, hence the asymmetric treatment. Section 4 explores existing implementations and experiences that will be reviewed, also referring to the publications where case studies, pilots, rankings, evaluations, and assessments are available.

A critical review and discussion of the literature analyzed is performed in Section 5. It places the developments in Smart Grids and Smart Cities and the corresponding components in the context of the energy trilemma mentioned above, structures them in relevant modeling layers, and associates those components with specific characteristics of Smart Cities. The deployment of Smart Grids in the context of Smart Cities is also discussed, considering a gap analysis by identifying areas that seem to have received less attention and proposing topics that could deserve reinforced research and activity. Finally, the conclusions are drawn in Section 6.

2. Basic Concepts

2.1. Smart Cities

Several formal definitions of Smart Cities exist originating from multiple entities, such as “A Smart City is a place where traditional networks and services are made more efficient with the use of digital and telecommunication technologies for the benefit of its inhabitants and businesses” by the European Commission [8], or “A Smart Sustainable City is an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, the efficiency of urban operation and services, and competitiveness while ensuring that it meets the needs of present and future generations with respect to economic, social, and environmental aspects”, by the United Nations [9]. It is often associated with a broad scope of sectors and domains, as described below. As highlighted in [10], it is hard to define a Smart City, as cities claim to be “smart” based on multiple criteria, such as novel e-governance schemes, community engagement programs, sustainable living, and the use of ICT for innovative applications. Smart Cities are often engaged in pursuing climate goal achievements, economic development, and societal impact and rely on components such as smart transportation, smart agriculture, smart energy, smart infrastructure, Smart City services, smart homes, smart health, and smart industry [10].

Smart Cities and communities focus on the intersection between energy, transport, and ICT [11]. In this line, intelligent cities, virtual cities, digital cities, and information cities are all perspectives that have ICT as a central element to the operation of future cities [12].

It is claimed that Smart Cities [13] bring digital intelligence to the urban context, solving public problems and achieving a higher quality of life. The claim goes on structuring three layers of “smartness”: on top of the traditional infrastructure (physical and social), there exists a technological layer that includes a mesh of connected devices and sensors connected by high-speed communication networks (layer 1), smart applications and data analysis capabilities, translating raw data into insights and actions (layer 2), and finally, a level of adoption and usage, often leading to better decisions and behavior change (layer 3).

Besides the view of Smart Cities from an ICT and digital perspective, the concept is also frequently associated with the extensive use of renewable energy and sustainability from the environmental point of view [11,14,15]. This is to say that the concept of sustainable cities is also often associated with Smart Cities [2,6,16]. However, according to [11], the Smart City framework puts a much stronger emphasis on modern technologies and “smartness” than the urban sustainability frameworks. On the other hand, it is identified that urban sustainability frameworks address more often indicators measuring environmental sustainability than the Smart City framework does, which opts to highlight social and economic aspects. Smart Cities, thus, aim to improve sustainability with the support of technology. Indeed, Smart Cities leverage technology to optimize energy usage in buildings, transportation, and infrastructure. This includes using renewable sources like solar and wind energy for power generation. In [17], it is claimed that Smart Cities contribute to sustainability and improved urban quality through the creation of an environment of effective and efficient services that use digital technologies, communication technologies, and data analytics.

Regarding definitions, more than 100 definitions were identified in [7] by the International Telecommunications Union already in 2014. Furthermore, ref. [17] compiles 34 definitions of Smart City, where the authors note that the development and implementation of Smart City initiatives by policymakers, city planners, and stakeholders would benefit from a clear definition.

The topic of Smart Cities has been addressed from many angles, namely environmental, sustainability, conceptualization, governance, and planning, holistic, technological, carbon-neutral/self-sustainable concepts, percentage of usage of endogenous resources, etc. [18,19,20]. Indeed, as the authors highlight in [21], the multi-disciplinary cooperation of researchers and practitioners from different areas, such as architecture, computer science, civil engineering, electrical, electronic, and telecommunication engineering, social sciences, and behavioral sciences, is needed for a successful, effective, and sustainable implementation of the Smart City paradigm.

Furthermore, moving from a more technological view to a more encompassing perspective, ref. [22] addressed the idea that cities are too much focused on the digital rather than embracing a holistic approach to what is smart, in the perspective of the thesis of the “embedded intelligence of smart cities”.

Several Smart City literature reviews were performed, such as the eleven literature reviews published between 2015 and 2019 and analyzed by [23]. While some of them concentrate on specific aspects, such as conceptualization, governance, or planning, others take a holistic approach with no particular focus. The authors concluded that the scope and the depth of the key areas of Smart City research are not yet well captured. Other approaches follow a bibliometric analysis as in [14] or a scientometric analysis as in [6], providing an objective and statistical analysis of the research landscape and of existing relationships in the field of sustainable energy systems for Smart Cities. Additionally, ref. [24] presents a comprehensive and systematic literature review on emerging applications of machine learning methods to Smart Cities.

2.2. Smart Grids

The smart energy domain is one of the most challenging areas of Smart Cities research [25,26,27]. The authors of [15] further state that energy, essential for modern societies and cities, is delivered through multiple networks, with the electricity grid playing a key role. This role is expected to expand in the near future, a trend referred to as the “electrification of society”.

In line with this view, the authors of [7] advocate that smart energy and electricity networks are crucial components in the development of Smart City frameworks. Integrating these networks in a cohesive and coordinated manner within Smart City planning is essential and should involve a thorough assessment of their environmental, energy-related, economic, and social impacts, along with the application of cost–benefit analysis (CBA) to guide implementation decisions.

There are also several formal definitions of a Smart Grid from different entities, such as “A Smart Grid is an electricity network that uses advanced technologies to monitor and manage the transport of electricity from all generation sources to meet the varying electricity demands of end-users, while improving efficiency, reducing costs, and increasing system reliability”, by the International Energy Agency [28]. As opposed to the Smart City concept and definitions, the scope is limited to the energy field and sector. However, it spans beyond the realm of a city.

According to [29], Smart Grids integrate ICT into electricity networks, helping to achieve CO₂ emissions reduction by optimizing the integration of fluctuating renewable energy sources (RESs) in the grid. They also enhance energy efficiency by coordinating the integration of multiple loads and generation assets in real time, improve the security of supply via improved automation and control that manage needed grid reconfigurations, and encourage the participation of consumers in energy markets.

The authors of [30] claim that with the advent of Smart Grids, the traditional electric power grid has been evolving from an electromechanically controlled system to an electronically and digitally controlled one. With this evolution, the traditional grid planning and operation activities can be structured in the following manner: (1) monitoring and measuring processes, communicating data to operation centers, and responding automatically, making use of field devices, digital sensors, communication technologies, and automation and control devices; (2) information management, via the sharing of data between appropriate devices and systems; and (3) processing information to help operators to make use of such available data [31]. This brings new insights into how to address problems from forecasting of loads to stability of the power grid, from detection of faults to security of the grid.

Smart Grids further make use of ICT technologies to provide utilities with improved observability of the grids and real-time data at multiple points of the grid, such as consumers’ energy usage, generation profiles, and network conditions. This visibility allows for appropriate planning and management of distributed generation at consumers and utility sides, as well as efficient and effective self-healing and reconfiguration of grids [10].

The authors of [29] recall that traditional electricity networks are characterized by a few large power plants with high levels of CO₂ emissions, a large number of local consumers with stable loads, a unidirectional flow of energy, and centrally managed and controlled by a few large network operators. On the other hand, Smart Grids emerge in a context of fluctuating (renewable) electricity sources, witness a bi-directional flow of energy between decentralized sources and loads, and react in a flexible way to balance generation and load. This is enabled by an “energy information system” coordinating a complex network of producers, consumers, and storage units. This concept is further expanded to other energy-related aspects besides electricity.

2.3. Bringing the Concepts Together

The authors of [32,33,34] attempt to provide a link between the concept of Smart City and Smart Grid. They claim that a Smart City addresses multiple dimensions, such as governance, mobility, health, energy, and economy in general, to promote a sustainable urban life, ensuring the integration of critical infrastructure and multiple stakeholders. As such, the concept of Smart Grid is embedded in the concept of the Smart City in that it contributes to modernizing traditional power systems.

Also, in [35,36], Smart Cities and Smart Grids are strictly correlated, with a Smart Grid sitting at the heart of the Smart City. According to the authors, energy-efficient transportation and management need reliable, sustainable, and resilient infrastructures. Thus, the existence of an urban Smart Grid depends on the availability of the characteristics of a Smart City. Conversely, developing a Smart City requires the enhancement of the traditional electricity network via the deployment of technologies such as sensors, transducers, and actuators.

The authors of [37] identify that the benefits of developing Smart Grids in Smart Cities are widely acknowledged and that Smart Grids empower Smart Cities through several characteristics:

• The modernization of power systems in Smart Cities via real-time monitoring, automation, control, and self-healing capabilities. Energy supply is more reliable and sustainable in a transformed urban setting with smart buildings and houses;
• The improved air quality and reduced carbon emissions due to the ability to integrate more distributed renewable energy resources and to facilitate the electrification of transport;
• The connection of demand response services and responsive distributed generation, allowing providers and consumers to reduce costs and thus achieving economic benefits;
• Quasi-real-time participation of consumers in the electricity market, making the development of new products and services facilitated;
• Smart electricity management, together with bi-directional interactive service platforms, improving social service friendliness, as desired in Smart Cities.

The authors of [3] clarify that Smart Grids are important in the processes of triggering Smart Cities by addressing the development of micro/nano-grids and the deployment of renewable energies, energy storage technologies, and smart water systems in urban areas. The authors claim that the impact of Smart Grid in Smart Cities includes two aspects: (i) new business models for distribution system operators associated with the management of microgrids, prosumer actors, islanding modes of parts of the grid and energy management systems, and (ii) new concepts in Smart Grid domain affecting Smart City concept, namely urban mobility, vehicle-to-grid solutions, and smart lighting.

Both the concepts and the deployment processes of Smart Grid and Smart Cities present clear similarities. Both make extensive use of ICT applications to enable devices and systems that are remotely monitored and controlled. From the tangible deployment perspective, both are infrastructure intensive, often requiring significant financial investments, and tend to have longer payback periods [38]. Regarding the role of ICT in both concepts, ref. [32] highlights that Smart Grids and Smart Cities can coordinate, control, and monitor the city’s power demand and energy consumption, either to reduce peak demand on utilities or to schedule consumption during periods of lower electricity prices.

From the discussion above, it is clear that Smart Grids in a city are instrumental in interconnecting several of the elements that pertain to a Smart City. Figure 2 captures key elements (not exhaustive) pertaining to Smart Grids and Smart Cities and their intersection (shaded elements). Although the boundaries are not rigid, there are elements of Smart Grids that are not key to Smart Cities (such as novel technologies such as Flexible Alternating Current Transmission Systems—FACTSs, virtual power plans—VPPs—that span across multiple geographies, or use of advanced imagery technique to monitor large distance overhead lines—OHLs) and there are elements of Smart Cities that are not key to the concept of Smart Grids (despite all activities needing energy).

Besides the positive synergies that can exist between the two concepts, it is important to analyze other angles as well. As mentioned in [7], while the envisioned transformation may bring many benefits, it may generate conflicts between the different objectives, concerns, and interests of different stakeholders, both at an individual level and at community and societal levels. For example, while Smart Grids provide the ability to manage vastly distributed generation, enabling large quantities of dispersed renewable sources and local microgrids, it will make the planning and operation of the distribution and transmission grids more difficult due to fluctuating infeed, bi-directional power flow, and behind-the-meter generation. Smart Grids will also stimulate an increase in demand (for example, via electric vehicles and electrification of loads) and peer-to-peer exchanges of electricity among end users (for example, prosumers and energy communities), among other challenges that can be technical, legal, or regulatory.

In [39], a new forecast anticipates that global spending on Smart Cities initiatives might have reached USD 189.5 billion in 2023. Those initiatives focused on resilient energy and infrastructure projects, data-driven public safety, and intelligent transportation. Five use cases are expected to represent the most spending, namely Smart Grid, fixed visual surveillance, advanced public transportation, smart outdoor lighting, and intelligent traffic management. Although responsible for more than half of all Smart Cities spending in 2019, their share is expected to decline with growing expenditure on vehicle-to-everything (V2X) connectivity, digital twin, and officer wearables. This reinforces the strong link between Smart Cities and Smart Grids, not only at the academic level but also at the societal level.

To reinforce this point still at another level, the EU Covenant of Mayors for Climate & Energy, launched in 2008 in Europe, is an initiative supported by the European Commission, gathering thousands of local governments aiming at securing a better future for their citizens. By opting into the initiative, they willingly embrace EU climate and energy goals, creating yet another link between the concepts of Smart City and Smart Grid.

Despite the proximity of the Smart City and Smart Grid concepts, the literature above does not provide a clear and systematic view of the elements and aspects that are key in the intersection between both concepts. Also, it is important to understand how performance evaluation metrics cover that intersection and what can be extracted from case studies and best practices.

3. Key Components of Smart Grids in the Context of Smart Cities

In this section, we identify 16 key components that emerged from the literature review as essential aspects associated with the concept of Smart Grids in the context of Smart Cities. These components are diverse in nature, ranging from infrastructural elements to functionalities and tools. It is in this broad sense that the term “components” should be understood, with the underlying claim that they contribute to both Smart Grids and Smart Cities. In order to provide a structured view of all the components, they were categorized in the following manner: communications infrastructure (3.1 to 3.4), power systems specific (3.5 to 3.12), and data and digital tools (3.13 to 3.16).

3.1. Advanced Metering Infrastructure (AMI)

Advanced Metering Infrastructure (AMI) was and still is one of the first and most visible relationships between Smart Cities and Smart Grids. It addresses the challenges of managing a system via a two-way information exchange between the grid and the consumer [38,40]. In simple terms, the deployment of smart meters offers real-time insights into electricity usage, including dynamic pricing details and associated carbon emissions, thereby empowering both users and stakeholders to make more informed decisions [3,27,35]. Multiple operational benefits, with resulting cost savings and increased convenience for consumers, are highlighted in [31].

Smart Cities deploy advanced metering systems that provide real-time data on energy consumption and production. This enables more accurate billing and demand forecasting and allows consumers to monitor and manage their energy usage.

Also, ref. [16] refers to AMI as the usage of a smart metering infrastructure composed of sensors located on consumers’ access points and across the electricity flow chain on production, transmission, and distribution systems. Incorporating smart meters, remote controls, and communication technologies into power grids enhances their efficiency, reliability, and sustainability by using the self-monitoring and feedback mechanisms they provide. This is in line with the concept that distributed energy systems are increasingly able to manage and maintain themselves using monitoring and automation, and energy system management activities are dynamically reorganized and coordinated.

3.2. Information and Communication Technology (ICT) Infrastructure

A robust ICT framework that enables seamless communication and data exchange between various urban systems and stakeholders is central to Smart Cities [2,6]. Throughout the years, the existing urban power grid has been progressively integrated with the developing communications networks [4]. The Smart Grid brings the ability to have real-time bi-directional communication between multiple energy agents to the realm of the city [26].

The authors of [41,42,43] present an extensive analysis of networking architectures and protocols for applications in Smart Cities, including 5G, WiMAX, ZigBee, LTE, and LoRA, among many others. The details go beyond the nature of this review. In particular, the deployment of high-speed 5G networks is expected to revolutionize Smart City capabilities, enabling real-time data transmission, augmented reality applications, and enhanced connectivity.

Also, this aspect has already been appropriately addressed before when introducing the concepts of Smart City and Smart Grid. However, it is worth mentioning the remark in [44] that employing ICT in a city context does not by itself define a Smart City.

3.3. Grid Modernization and Sensor Deployment

Smart Grids incorporate sensors and monitoring devices throughout the grid infrastructure. These sensors provide real-time data on grid conditions, allowing for proactive maintenance and quicker response to faults, as well as on generation and consumption, allowing efficient and effective energy management [26].

According to [35], advanced sensing systems and smart transducers are deployed to improve the performance of the electric grid and city services [2,10]. This is particularly important as the electric system is experiencing a radical transformation with the increase in distributed renewable energy generation. This development has modified the traditional energy flows in the grid, as it may (suddenly) change direction depending on locations and amounts of generation and consumption. Also, future grids must manage possible fluctuations and interruptions in this type of generation to prevent blackouts. Sensors and transducers will have a key role in providing information on each node of the electric grid, allowing real-time monitoring and optimization of energy management [26]. That rich information will allow the dynamic (re-)configuration of the grid to respond to events and patterns on the generation and consumption sides and on the grid itself. Finally, it is important to note that power quality characteristics and disturbances are particularly relevant in many industries and critical services such as healthcare (hospitals). These include harmonics, voltage fluctuations, overvoltage and over-current impulses, micro-outages, and break length, which can cause improper functioning of several power components and loads. Therefore, energy with specific power quality might have to be ensured from a specific node of the grid to the node where the consuming facility is connected.

Advanced sensors should be able to provide measurements and also process data in real time, thus allowing increased insights into the energy flow. In this process, it is important to take into consideration the efficiency of the mechanism for information and data sharing, sensor reliability and robustness, the need for standardization to ensure interoperability and plug-and-play capabilities, and low-power, low-overhead data transmission schemes [10,35].

Grid modernization at a city and customer level contributing to the development of a Smart City also encompasses aspects related to single-customer microgrids, buildings EMS, behind-the-meter, and community storage and security [31].

3.4. Internet of Things (IoT)

Closely related to the topic above, although not necessarily specifically from the electrical grid point of view, the emerging topic of IoT deserves particular attention in this context [33]. These technologies form the backbone of data collection and analysis in Smart Cities. Sensors embedded in urban infrastructure gather real-time data for monitoring and decision-making [2,6,7]. The topic is also closely related to edge computing. The processing of data closer to the source allows the reduction in latency and bandwidth requirements, making it a crucial technology for real-time applications in Smart Cities.

As indicated in [10], the Internet of Things (IoT) is a system that integrates different devices and technologies, making unnecessary many of the human interventions. Furthermore, it pertains to the widespread interconnection of devices via the internet, enabling them to transmit data to the cloud or other devices and possibly receive instructions to carry out specific tasks. Making use of diverse technologies and allowing interactions between them at multiple capilar levels of the energy grids, IoT stimulates the development of Smart City systems for the different benefits in the different areas of urban living mentioned before [45]. The authors claim that IoT can be central to the multiple Smart City initiatives, being an enabling technology that allows the widespread digitization that is strongly associated with the concept of Smart Cities.

Still, according to the authors, Smart City applications can typically have five aspects associated with them, namely the collection of data, its transmission/reception, storage, its analysis, and, eventually, actuation.

Other authors, such as [46], go even further, considering that Smart Cities is a global term for several IoT-based subsystems such as Smart Grids, smart buildings, smart factories, and intelligent transportation, and present a comprehensive analysis of challenges and current solutions for using IoT in the energy sector. According to the authors, in a Smart City equipped with IoT-based Smart Grids, different sections of the city can be connected together.

Addressing a specific application in a household environment, the authors of [46] identify that household energy use within the residential sector can include various activities such as lighting, appliance operation, water heating, cooking, refrigeration, as well as heating, ventilation, and air conditioning (HVAC). Since HVAC systems generally represent around 50% of a building’s total energy usage, effectively managing them is crucial for lowering electricity consumption. As technology continues to evolve, Internet of Things (IoT) devices are becoming increasingly significant in controlling energy losses associated with HVAC operations.

In a more industrial setting, the authors mention that the installation of sensors on specific components of a site allows one to identify which ones consume more energy than their nominal level. This way, Energy management within the facility can be effective, component malfunctions can be addressed, and energy usage can be optimized. These improvements contribute to minimizing energy waste, resulting in both economic savings and positive environmental effects.

IoT devices allow the monitoring and analysis of energy usage patterns across several city infrastructures, such as public lighting, transportation, and buildings [26].

The IoT concept can also provide a framework for energy management systems, as mentioned in [35], where local data from the city are accessed through the cloud via remote access using the IEC61850 protocol.

As a caution note, ref. [46] also remarks that managing the energy demands of IoT devices presents a significant challenge, particularly as the deployment of these technologies is expected to expand rapidly in the near future. Supporting billions of connected devices will require substantial energy resources, and the sheer volume of devices will contribute to a growing problem of electronic waste. Addressing these issues calls for the development of energy-efficient, low-carbon communication networks. In response to these concerns, the concept of Green IoT (G-IoT) has emerged. G-IoT focuses on minimizing energy consumption across the entire lifecycle of IoT devices, including their design, manufacturing, deployment, and eventual disposal.

3.5. Renewable Energy Integration

Integration of renewable energy sources is of great importance for the development of Smart Cities and is often mentioned as one of its attributes [6]. Smart Grids can facilitate the integration of renewable energy sources, such as solar panels and wind turbines, by efficiently managing the intermittent nature of these resources, making use of available data, adequate logic, and real-time communication [3,4,7,26,29], and using resilient power technologies and solutions [27,33]. However, the authors of [47] note that the increasing replacement of synchronous generators with such systems affects grid stability as synchronous generators inherently provide the inertial response needed in power systems. Also, in [32], the issues related to load flows, voltage control, fault levels, and network security are identified. Smart Grid features can help active power controllers for renewable energy sources (RESs) in future grids dominated by inverters, enabling them to deliver synthetic inertia responses. This capability is crucial for stabilizing frequency fluctuations and ensuring grid synchronization in systems with a high level of RESs. Moreover, integrating various energy assets—such as solar photovoltaic (PV) systems paired with energy storage technologies—or adjusting generators to operate below their maximum power output can effectively provide synthetic inertia during instances of under-frequency conditions. Additionally, storage also adds to the issue since it is also an inverter-based resource. Smart Grids also enable bi-directional energy flow, allowing excess energy to be fed back into the grid and ensure adequate power grid balance [25].

3.6. Distributed Energy Resources (DERs)

Additionally to the above renewable energy sources, Smart Grids support the integration of DERs in Smart Cities, including small-scale renewable generation, energy storage systems, electric vehicles, and microgrids. These resources enhance grid flexibility and resilience.

As extensively discussed in [4,26,33], distributed energy resources (DERs), combined with adaptable resources linked to urban power networks, play a vital role in enhancing flexibility at both the generation and consumption sides, being crucial for reducing carbon footprint and improving city [27] or campus [48] energy management. Within a Smart City framework, DERs and flexible resources encompass a range of technologies, including decentralized energy sources (such as distributed solar photovoltaic systems, wind turbines, and fuel cells), energy storage solutions (like lithium-ion batteries, supercapacitors, flywheel storage systems, thermal energy storage, hydrogen storage, and other emerging technologies), and flexible demand-side resources (such as electric vehicles, load switching stations, controllable loads, and various other adaptable energy consumers). Additionally, flexible prosumers—entities that both produce and consume energy, such as solar PV users, smart buildings, and VPPs—contribute to this dynamic energy ecosystem. The system is further supported by advanced grid technologies, including intelligent soft switches, flexible switching devices, energy routers, solid-state transformers, next-generation converters, and other smart, controllable infrastructure designed to enhance grid adaptability.

The authors of [3] address several aspects related to the integration of solar energy. The discussion covers issues such as managing low-frequency power fluctuations in centralized PV-grid configurations, strategies for compensating reactive power within the system, and the development of control methods for active PV systems that incorporate components like ultra-capacitors, batteries, and photovoltaic panels. Additionally, they examine the operational capabilities of PV micro-inverter microgrids, focusing on their ability to function effectively in both islanded and grid-connected modes through a coordinated management approach, among other related topics.

Furthermore, the authors refer to how wind energy can help increase efficiency and reliability via a fully distributed economic dispatch algorithm. Also, using a stochastic-based algorithm and other distributed resource contributions such as vehicle-to-grid (V2G) service, it is possible to minimize in a smart way the wind power variability and reduce the costs associated with charging and discharging of electric vehicles.

Also, in [26], it is addressed how Smart Cities are resorting to technological advancements provided by Smart Grids to integrate multiple resources such as small-scale renewable generation assets, storage devices, and electric vehicles.

Additionally, ref. [3] evidenced the use of energy storage in the context of Smart Grids in localized areas such as Smart Cities. They addressed particularly the aspects of balancing supply and demand in the presence of renewables and peak shifting.

Finally, the same authors also mention the use of battery electric vehicles and plug-in hybrid electric vehicles as flexible, distributed energy storage systems that can operate in a vehicle-to-building (V2B) mode. The existing results show that employing BEVs (Battery Electric Vehicle)/PHEVs (Plug-in Hybrid Electric Vehicle) for peak load shifting can lead to a reduction in electricity costs for both consumers and vehicle owners.

3.7. Integration with Energy Storage Systems

Smart Grids in Smart Cities make use of energy storage systems (e.g., batteries) to store excessive energy generation (for example, by renewables) during periods of low demand. The stored energy can be later used during periods of peak demand or in the event of grid outages [3,4,26,48].

Although integration of storage is also more globally addressed in the context of distributed energy resources (DERs), as mentioned above, many authors address it specifically. For example, in [32], various applications of distributed storage within local power systems. These applications play a key role in alleviating network congestion, postponing the need for infrastructure upgrades, reducing peak demand, managing energy costs based on time-of-use pricing, supporting voltage stability, enhancing the firming capacity of variable renewable energy sources, facilitating the integration of renewables into the grid, providing reserve capacity, and improving both power quality and system reliability. Storage also has a key role in operation under islanding mode [3] or mitigating power outages [27], allowing (critical) segments of a city, such as hospitals or campuses, to continue operation if supply from the main grid fails.

Relevant also is the complementarity of storage with city infrastructures (e.g., residential and commercial buildings, campus) and assets (e.g., vehicles), as highlighted in [7,26].

3.8. Grid Automation and Self-Healing

Smart Grids are equipped with automation systems that can detect and respond to disruptions or faults in real time. This allows for faster restoration of power and reduces the impact of outages on city residents and businesses [3] by means of self-healing capabilities [26,27].

As discussed in [49], when a fault occurs in a distribution system, it needs to be isolated. Once isolated, the system can be reconfigured to restore the affected area. This reconfiguration involves making changes to the way power is distributed using different feeder lines. Self-healing involves automated processes to identify faults, isolate them, and restore power to affected areas with minimal human involvement, depending on the level of automation in place. After identifying a fault, the optimal restoration plan is developed to minimize the number of areas without power while adhering to system and operational limitations. Fully automated self-healing systems are only feasible within Smart Grid environments, where remote-controlled equipment is used to isolate faults, manage loads, and adjust the system’s layout. The authors discuss an adaptable agent-based model to simulate Smart City dynamics and describe a multi-agent system model for self-healing protection. Automation and self-healing increase grids’ reliability and minimize both outage time and areas following a fault by employing distributed control methods [10]. Effective decision-making relies on gathering sufficient information, making communication crucial for multi-agent-based restoration, with the communication infrastructure operating independently from the physical layout of the smart distribution network.

Substation automation, in particular making use of communication protocols such as IEC61850, is a key feature of Smart Grids in these processes [42]. Such grid automation in cities contributes to automated location of failures, equipment health monitoring, and voltage optimization [31].

3.9. Resilience Enhancement

Paper [4] focuses on a comprehensive analysis of strategies for developing a resilient power grid tailored to Smart Cities. In particular, the authors review the high impact low probability (HILP) extreme events categories that pose risks to the grid, categorizing them into extreme weather and natural disasters, intentional malicious events, and societal crises. The authors discuss several available approaches to enhance resilience, namely making use of microgrids, active distribution networks, distributed and flexible energy resources, multi-energy integrated systems, and cyber–physical systems. They also address emerging methods, such as probabilistic forecasting and analysis and AI-driven approaches.

The authors of [47] claim that, although resources like photovoltaic (PV) solar panels and wind farms produce power intermittently, integrating these and other renewable energy sources (RESs) into the power grid helps improve both system security under normal operating conditions and overall resilience before, during, and after extreme events. Research has shown that utilizing distributed assets—such as solar PV arrays, battery-based storage solutions, and flexible demand services—can significantly boost the grid’s ability to endure and recover from catastrophic incidents. Addressing essential design questions remains crucial, however, including how best to position and operate these resources to maintain critical city infrastructure. By ensuring continuous or rapidly restored electricity to vital services following major disruptions, self-sustaining microgrids can be formed during outages to support resilient Smart City developments.

The authors identify that several Smart Grid projects have successfully addressed specific facets of handling extreme events. Nonetheless, there is still no unified, all-inclusive platform that can quickly elevate the overall resilience of the industry. Recent studies emphasize the urgent need for robust methods to evaluate and improve system resilience. IoT and sensors are key elements to provide such resilience and reliability [10].

From the technological point of view, according to [47], the ideal solution involves creating flexible and adaptable platforms capable of addressing a broad and evolving range of risks affecting power networks and Smart City operations. These platforms would provide real-time and post-event insights, empowering utilities and municipal entities to optimally manage restoration and recovery processes by minimizing downtime, operational risks, vulnerabilities, and financial losses. In practice, this includes overseeing large-scale power systems and distribution feeders, supporting the formation of self-contained community microgrids with multiple points of common coupling to the grid, integrating diverse PV systems to supply power during outages, and incorporating behind-the-meter resources and consumer-owned renewable resources and storage.

3.10. Electric Vehicles (EV) and Charging Infrastructure

Regarding mobility in Smart Cities, emphasis is placed on creating efficient and sustainable transportation networks, including electric vehicles (EVs) and EV charging infrastructure, intelligent traffic management, and promoting public transit alternatives. This transition from fossil fuel-powered vehicles to electric ones is a key component of renewable energy adoption [50]. Smart Grids also support the deployment of EV charging stations, enabling the widespread adoption of electric vehicles, and these charging stations would be managed intelligently to balance demand on the grid.

As emphasized in [21], from a mobility perspective, Smart Cities need to be prepared for a widespread shift to electric vehicles. Although many carmakers are already equipped to bring plug-in Electric Vehicles (EVs) to the market, the planning, development, and installation of suitable charging infrastructure still presents a major challenge as it needs to ensure users’ requirements and avoid degradation of grid power quality. This involves different disciplines, such as grid analysis, traffic monitoring and analysis, and service simulation before field implementation. The existing literature is large and addresses issues of planning, operation, control, and interaction with the electricity market.

In an even more specific city environment, ref. [7] notes that connected electric vehicles can be seamlessly integrated with buildings, residences, and local grids by taking advantage of their onboard storage to provide demand response services. Although vehicle-to-grid (V2G) and vehicle-to-home (V2H) systems are already being introduced in various regions, additional exploration is needed to clarify the financial viability and business potential of these models. In those V2H scenarios, the vehicle can act as residential battery storage. This setup allows it not only to recharge using power generated by the home’s own renewable sources but also to supply backup electricity during off-grid scenarios or to cover part of the necessary load. Also, the EV can serve as a reserve generator in local microgrids, bolstering reliability and resilience.

To tackle some of the issues related to the dimensioning and dynamics of such installations, the authors of [21] present a case where each cluster consists of ten individual charging points rated at 50 kW, while each feeder has an automated system that can dynamically lower the charging power in order to prevent congestion on the network.

3.11. Integration with Smart Buildings and Homes

Smart Grids interact with intelligent building and home energy management systems, allowing for coordinated control of energy usage and generation within individual buildings, contributing to overall grid optimization [51].

As discussed in [3], within a Smart City framework, the advent of smart energy systems and grids brings the ability to effectively manage the energy usage of modernized buildings by interconnecting them with Smart Grid infrastructure. This linkage aims to optimize both power consumption and on-site generation. The authors argue that buildings are evolving beyond mere structural enclosures to become data-intensive environments that monitor and analyze physical conditions—such as temperature, airflow, and occupancy—for different purposes. These include improving energy performance, optimizing space utilization, and adapting in real time to both internal dynamics and external conditions.

This is reinforced in [7], where it is claimed that Smart houses address both the in-house domain, improving living conditions of households across multiple aspects, such as heating and cooling, comfort and new services, and in the external outer reach extending their capabilities with networked controls and remote access, such as security, shopping, and health. Extended benefits can be obtained in the context of multiple houses in smart neighborhoods and communities.

3.12. Microgrids

With the increasing integration of distributed energy resources (DERs) in the current Smart Grid, direct current (DC) networks have emerged as a prominent feature in the industry. A set of assets such as load and generators in a confined area, such as a university campus, hospital compound, or industrial complex, acting as a microgrid and with the ability to connect and disconnect from the grid have several implications in the development of Smart Grids in cities [52], as already extensively discussed in the paragraphs above. As a specific example, hybrid AC/DC microgrids consisting of renewables and energy storage combinations, including supercapacitors, covering ultra-fast response, and lithium-ion batteries addressing moderately long-term load buffering with real-time energy management are presented in [53]. The proposed algorithms aim at the minimization of the impact of short-term pulses on stability. These aspects are discussed also in [54].

As described in [32], with the expanding adoption of small-scale renewable energy technologies, namely solar panels, a new type of user has emerged: the prosumer, who both consumes and generates electricity. When their production surpasses their consumption, prosumers can feed the excess power back into the grid, making the integration of renewables and effective energy management strategies even more essential. These prosumers frequently collaborate to form microgrids—localized networks capable of operating independently in so-called “islanding” mode or remaining connected to the main power grid. Increasingly, authors indicate that it is anticipated that Smart Cities and neighborhoods will incorporate this “islanding” feature in their microgrids, enabling them to sustain local electricity needs using distributed energy resources for multiple hours if the main grid experiences an outage. Effective microgrid management not only improves reliability but also introduces potential new business models and revenue streams for distribution system operators that oversee serving uninterrupted electricity. Figure 3 depicts schematically such a microgrid, for example, in a university campus.

3.13. Grid Monitoring, Data Analytics, and Artificial Intelligence

Smart Grids employ advanced monitoring and data analytics tools to continuously assess grid performance. This includes tracking voltage levels, line losses, and other key parameters to ensure efficient operation. The authors of [3] claim that traditional non-smart systems face limitations due to the absence of real-time monitoring and control. This gap presents a significant opportunity for Smart Grids to serve as an effective real-time management solution. Also, the authors of [15,55] defend that artificial intelligence (AI) usage in urban planning processes remains largely unexplored from the research point of view, identifying specific gaps and that Big Data is a central element for effectively using AI in urban planning and Smart Cities.

Regarding behavioral aspects, the authors of [12] remark that pervasive mobile devices allow citizens to have access to real-time energy prices and to adjust their consumption accordingly, thus reducing stress on energy costs and on the grid.

The monitorization analytics capabilities allow, according to [16], real-time tracking of energy usage and greenhouse gas (GHG) emissions across different spatial and temporal dimensions, stimulating a reduction in energy usage and thus mitigating GHG emissions. To achieve this, the Smart Grid needs to gather and analyze a wide range of data from various power sources and processes in real time. These data are then used to make informed decisions by the processes control and improve the performance of the power system. Moreover, the Smart Grid enables the management of other distribution automation devices to enhance the efficiency, reliability, and sustainability of power production and distribution.

The authors of [24] present a comprehensive and systematic literature review on a specific area (machine learning methods) in the context of Smart Cities. The application domains include aspects such as energy, healthcare, transportation, security, and pollution. The authors analyzed machine learning (ML) techniques being used by different sectors in the period 2016–2022 and realized that Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM) were still the techniques mostly used for energy-related applications rather than more advanced ML models (hybrid, ensemble, deep learning) probably due to a compromise between complexity, accuracy, and processing speed.

The research revealed that hybrid models and ensembles outperformed other methods by achieving a balance between high accuracy and low overall cost. In contrast, deep learning techniques demonstrated superior accuracy compared to hybrid models and ensembles but required more computational power. Additionally, all advanced machine learning approaches exhibited slower processing speeds compared to single methods. While support vector machines and decision trees generally outperformed artificial neural networks in terms of accuracy and other metrics, the difference was minimal. Therefore, it can be concluded that either of these approaches is suitable for use.

A more recent study [55] highlights that AI has significant contributions in all four domains of energy generation, distribution, transmission, and consumption, leading advancements in generation and consumption forecasting and intelligent control systems.

In [56], the authors present a study addressing the optimization of energy consumption in Smart Cities. By using a hybrid GA (Genetic Algorithm)-SVM technique, the performance in terms of specificity, sensitivity, and accuracy was 21% improved when compared with other single ML techniques due to the presence of the GA optimizer.

Following the rationale above regarding renewable energy integration being a key element in Smart Cities, power generation forecasting output from renewable energy sources (RESs) is crucial for optimizing energy management in Smart Cities, as highlighted in [57]. For this purpose, the authors present an architecture based on IoT designed for improved generation forecasts within smart microgrids. Their approach extends beyond power production and includes other power consumption sectors like transportation and healthcare, aiming for comprehensive and efficient overall energy management.

Forecasting renewable energy sources (RESs) generation using artificial intelligence (AI) algorithms plays a crucial role in enhancing and managing Smart Cities. Accurately predicting solar and wind power generation is vital for effective energy planning, integrating with the grid, and allocating resources optimally. Machine learning and deep learning methods offer sophisticated modelling techniques capable of capturing the intricate relationships and patterns found in solar and wind power data. By employing these techniques, accurate and precise predictions of power generation can be made, aiding in improved decision-making for energy distribution, load balancing, and managing demand in Smart Cities [58].

In the solution presented in [57], sensors are utilized to collect and store data related to wind and solar energy, with the measurements influenced by the inherent variability of weather conditions. Key parameters, such as wind speed, wind direction, and solar radiation intensity, are gathered sequentially, forming time series datasets. These historical records are then analyzed using various methods to improve the accuracy of both short-term and long-term forecasts. By incorporating weather prediction variables and simulating environmental conditions, statistical models are applied to capture the mathematical patterns within the data, enhancing the reliability of power generation forecasts.

In [25], key challenges that need additional research, particularly concerning optimization, the development of intelligent, adaptable networks, and the application of advanced computational methods powered by artificial intelligence and machine learning. The paper explores the implication of the adoption of AI in the context of renewable energy in future Smart Cities research, highlighting the following aspects: (i) the use of optimization algorithms throughout the entire renewable energy process, from generation to end-use, (ii) the application of artificial intelligence in renewable energy, both at the micro (smart home applications) and macro (optimizing energy Smart Grids) levels, (iii) the incorporation of sustainability principles, (iv) the creation of an AI-driven ecosystem for managing energy-related Big Data to support adaptable and integrated services and applications, (v) the advancement of cloud-based energy management solutions and (vi) the implementation of customized smart monitoring systems at the local scale.

The authors of [30] surveyed the topic of the usage of AI techniques applied to the reliability and resilience improvement of Smart Grid systems. In more detail, the authors discuss (i) load forecasting, further categorized into short-term, medium-term, and long-term; (ii) stability assessments, including transient stability, frequency stability, small-signal stability, and voltage stability; (iii) faults detection; and (iv) security. It is concluded that despite the application of AI to several crucial areas of reliability and resilience, further usage of AI is being hindered by issues such as privacy and security of data and the fact that many algorithms are not understandable or easily explained from a human perspective. These aspects are also addressed in [59,60,61,62].

3.14. Digital Twin

The authors of [63,64] highlight that, as modern urban environments advance, different energy-related domains—ranging from transportation systems to Smart Grids and microgrids—encounter challenges that complicate multi-layered energy management. In the context of transportation, traffic congestion demands constant monitoring, planning, and real-time analysis. Meanwhile, power grids strive for secure remote data transfer and the need for analyses based on real operational data. A practical solution to these issues lies in implementing and analyzing digital twin frameworks within each of these sectors.

As the authors elaborate, the digital twin (DT) is a digital model of a physical entity that mimics its physical behavior making use of platforms and real-time two-way data communication. The concept is based on an entity describing a complex physical system coupled with the real system through a communication connection. The concept of (quasi-)real-time updates between the physical and virtual environments (which can be in a single location, multiple, or in the cloud) in a closed loop is also a key feature of the DT. This way, the DT can provide information on assets such as location, condition, and real-time status based on the (quasi-)continuous update of information [43].

The authors address multiple uses of DT technology in the context of various dimensions of energy management in a city, namely transportation systems, connected power grids, and microgrids. In the microgrid domain, researchers have explored digital twin (DT) technology for a variety of purposes, including security, forecasting, monitoring and management, and fault detection. In addition, numerous DT-based network analyses have been examined, encompassing areas such as system restoration, reliability, predictive modeling, energy hub operations, uncertainty management, and both physical and cyber security. Using ML to establish a DT secure digital environment is also presented to address challenges such as security, standardization, connectivity, and data access and analysis.

In Figure 4, the concept of digital twin is depicted for the situation of a Smart Grid in the context of a Smart City.

3.15. Peer-to-Peer (e.g., Blockchain) Technologies

An “energy-sharing economy” is particularly suited to Smart Cities, given the high density of peer market agents [36]. Blockchain provides secure and transparent transactions, making it a valuable tool for managing Smart City data, ensuring privacy, and enabling secure financial transactions. The potential is also identified in [26]. In [43], blockchain is used in the traditional business model and in new disruptive models of the energy sector, contributing to optimize network organization and power systems. The authors argue that blockchain empowers distributed peer-to-peer trading among residents and energy assets, in particular those generated by sustainable power sources. These new interaction models allow the exploration of the creation of local energy markets aimed at enhancing energy efficiency, supporting the integration of renewable sources, expanding flexibility capabilities, facilitating more localized energy trading, and optimizing the balance between supply and demand—all while minimizing expenses. By examining these local market structures, we can better understand their significance for Smart Grids and geographically confined systems, such as microgrids, as well as their role in advancing sustainability objectives within Smart Cities [54].

3.16. Cybersecurity Protection

Given the interconnected nature of Smart Grids, effective cybersecurity solutions are crucial to protect against cyber threats and ensure the resilience of the grid against potential attacks or disruptions. Also, the vast amounts of data collected in Smart Cities raise concerns about privacy, security breaches, and the potential misuse of sensitive information.

As mentioned in [65], as communication networks, metering solutions, and smart control technologies continue to evolve—combined with the extensive use of internet-enabled platforms—modern power systems are undergoing incremental shifts in various, sometimes conflicting, dimensions within Cyber–Physical Power Systems (CPPSs) (also in [66]). Indeed, among the challenges of implementation of Smart Cities, as mentioned in [17], are issues associated with data privacy, ensuring equal access to technology, and the importance of collaboration between private and public sectors.

4. Smart City Developments and Deployments in the Context of Smart Grids

4.1. Context and Evolution

As indicated in [23], Smart City research goes back to the 1990s. The authors go further, defending that the first-generation “Smart City 1.0” was mostly focused on technical and economic aspects, addressing the wider adoption of digital technology (such as smart meters) and the economic benefits of Smart City projects. “Smart City 2.0” emphasizes a decentralized, human-centered model that encourages greater collaboration and active participation from the community.

The authors of [67] identify two main phases in the evolution of Smart City concepts. The first phase (1991–2015) in which scholarly interest in Smart Cities began to grow, while internet usage across different urban fabrics was still at an early stage. Consequently, discussions about security issues—particularly in what concerns data privacy and surveillance—were prominent within the civic context. They also note that while some technologies were starting to address energy needs, the overall number of publications remained limited, with terms like “Smart Grid” and “renewable energy” receiving relatively little academic attention at the time. During the second phase (2016–2021), research on Smart City topics surged, particularly in conjunction with the Internet of Things (IoT) and Big Data. This period also saw an increased focus on sustainability, governance, policies, and technological impacts. As part of this trend, the role of institutions in fostering “smartness” attracted more interest, and mobility innovations, along with emerging ideas like the 15 min city concept, gained significant traction among researchers.

Below, we review the selected literature on Smart City plans and implementations in order to identify how and to what extent the Smart Grid aspects are considered.

4.2. On Smart Cities Deployments

According to “Super Smart City: Happier Society with Higher Quality” [68], more than a thousand Smart City initiatives have been launched or are in progress worldwide since 2019. China is the clear leader, with about 500 projects under construction—far exceeding Europe’s total of around 90 Smart City endeavors, ranking second.

According to [43], Masdar Metropolis, in the United Arab Emirates, is often cited as the first city in the world with zero emissions and zero waste, relying on solar and wind resources to supply its power needs without generating pollution or CO₂ emissions. In Masdar, urban design strategies prioritize building orientation and shape to reduce the need for cooling, while carefully balanced light and shade in streets and open areas promote natural air circulation based on bioclimatic design concepts. Instead of using traditional compressor-based systems, the city relies on solar-powered condensation devices for cooling. Operating at full capacity, Masdar requires between 200 and 240 MW of electricity, which is entirely generated through renewable sources—80% of it coming from solar energy. This includes large-scale manufacturing facilities and the placement of solar panels on rooftops throughout the city. The operation and management of such infrastructure make use of the above-mentioned Smart Grid components.

Another example is presented in [29] regarding the city of Berlin, Germany. In 2015, the government introduced a Smart City Strategy outlining its plans to incorporate digital technologies across numerous facets of urban life over the coming years. As part of this initiative, Berlin’s municipal authorities identified ten designated “future sites” (Zukunftsorte) where digital solutions can be tested and showcased. These include a renewable-powered heating and cooling network that combines production and storage, a campus microgrid based on renewable energy, battery storage, and an electric vehicle fleet, and a technology park with a multi-energy microgrid and energy management system optimizing the system in terms of flexibility and CO₂ emissions.

These reveal three key concepts relating Smart Grid technologies to the city, presenting them as (a) a sustainability need to advance the local energy transition (“Energiewende”) of the city, (b) an economic necessity to ensure Berlin’s future as a flourishing metropolis and (c) a pioneering initiative for the modernization of the infrastructure of the city. “Urban Energiewende innovations” include VPPs, heating and cooling networks, V2G technologies, and other Smart Grid technologies.

In [68], the author analyzes Barcelona’s Smart City initiative that transformed the city’s strategy from focusing only on e-governance to having a Smart City at the center. In the energy dimension, the initiative addresses smart lighting, energy self-sufficiency, and smart mobility, requiring the Smart Grid components mentioned above for its efficient integration.

In [69], the European Commission tries to collect the best practices regarding Smart Cities across the EU. Over 80 cities across 19 countries are analyzed from a technology-driven perspective on energy, mobility and transport, and ICT, highlighting how these projects have influenced their local communities. In addition, the authors identify the economic, legal, and social hurdles encountered by these urban areas while offering both practical solutions and valuable insights drawn from their own experiences. In the Smart Grid dimensions, the cases include integration of district heating and cooling, electric energy storage for renewable energy generation, smart street lighting, smart control systems to integrate multiple energy vectors, integration of electric vehicles with the grid via V2G technology, demand-side response mechanisms, among other solutions.

The authors of [3] review the major European Smart City projects and identify that pilot projects in Europe have existed for more than a decade now. These provide researchers with an extended overview of the operation, monitoring, control, and evaluation of Smart Grid systems. In the period 2012–2019, a multiplicity of solutions, assets, and objectives were considered for the development of smart energy systems, including different generation types (e.g., PV, geothermal, hydro), storage solutions, grids (e.g., electric, heating and cooling, water), energy efficiency mechanisms, mobility solutions, and ICT involvement.

From a geographical footprint point of view, it is important to mention that these developments are not confined to developed countries and also have a strong presence in developing countries, as is the example of the program “100 Smart Cities Mission” was started by the government of India in June 2015 [40]. The Smart Grid components highlighted in the developments and deployments include electric vehicle integration, smart home interactions, solar and wind energy generation, substation automation, energy storage, and management of distributed energy resources.

It is also important to analyze the existence of pilots across the dimension of city sizes to identify any potential bias toward, for example, larger cities. In [3], it is reported that PLEEC (Planning for Energy-Efficient Cities) was conducted in 2014–2016 among six mid-sized European cities, including Eskilstuna (Sweden), Turku (Finland), Santiago de Compostela (Spain), Jyväskylä (Finland), Tartu (Estonia), and Stoke-on-Trent (England). The results provide evidence that compact urban structures and concentrated development facilitate efficient energy use.

Also, in [70], one initiative specifically targets the developmental outlook of Europe’s mid-sized cities. Despite most of the urban population living in such cities, the authors claim that research work focuses on the larger “global” metropolises. Therefore, the unique challenges these smaller urban centers encounter remain relatively underexamined, and they may lack the critical mass, resources, and organizational capacity available to major cities. We anticipate that small cities may experience some of the following exemplifying challenges:

• Economical and financial constraints: high initial costs due to upfront investments for Smart Grid infrastructure, such as AMI, sensors, and communication networks, that can be prohibitive for small city budgets; limited funding due to a smaller tax base and fewer financial resources making it harder to secure capital for large-scale grid modernization projects; uncertain cost–benefit results due to smaller energy markets and population densities;
• Technological challenges: legacy infrastructure, as many small cities rely on an outdated grid infrastructure that may not be compatible with Smart Grid technologies; limited ICT infrastructure and weak communication network that may jeopardize real-time data transmission requirements from a Smart Grid;
• Human capital and skill shortage: limited access to technical experts for planning, implementing, and maintaining Smart Grid systems; limited capability to provide continuous training for their workforce on emerging Smart Grid technologies;
• Cybersecurity and Data Management risks: limited budgets for advanced cybersecurity measures make small city grids more vulnerable to cyber-attacks;
• Operation and Maintenance issues: small grids may lack redundancy, making them more sensitive to disruptions or failures in Smart Grid components; reliance on external vendors for technical support can be costly and limit operational flexibility.

Some potential solutions to address these challenges can be identified and deployed. For example, public–private partnerships can leverage collaborations to share costs and technical expertise. Also, modular implementations can adopt scalable, phased approaches. Government incentives can explore grants and subsidies tailored to small cities. Finally, regional cooperation may allow sharing of resources and expertise across neighboring municipalities to reduce costs.

It is worth mentioning that the authors of [70] criticize the missing tangible results from pilots and real implementations in the literature, such as consumer behavior modifications, cost reductions, and consumption reductions, among other key metrics.

Table 1. Examples of Smart Cities and key features related to the energy domain.

Smart City	Key Features
Masdar United Arab Emirates [43]	Zero emissions
	100% renewable energy (wind and solar)
	Solar heating and cooling
	Building orientation and form
	Use of light and shades
	Bioclimatic design concepts
	Operation and maintenance with Smart Grid concepts
Berlin Germany [29]	Digitalized technologies
	Renewable-powered heating and cooling network
	Combined production and storage
	Microgrid based on renewable energy, battery storage, and electric vehicles
	Multi-energy microgrid and energy management system optimizing the system in terms of flexibility and CO2 emissions
Barcelona Spain [68]	Smart lighting
	Energy self-sufficiency
	Smart mobility
	Efficient integration via Smart Grid components
Cologne Germany [69]	Electrification via heat-pumps
	Efficient lighting
	Electric charging infrastructure
	Smart meters
	Demand response
	Dynamic pricing models
	Virtual power plant
Manchester United Kingdom [69]	On-site generation, storage, and integration
	Electric mobility
	Building energy management system
Sonderborg Sweden [69]	Retrofitting of building envelope for reduction in energy consumption
	Electrification via heat-pumps
	District heating and cooling
	Integration of wind and solar
	Electric Vehicles
Europe (several [69])	District heating and cooling
	Electric energy storage for renewable energy generation
	Smart street lighting
	Smart control systems to integrate multiple energy vectors
	Integration of electric vehicles with V2G (vehicle-to-grid) technology
	Demand-side response mechanisms
India (several [40])	Electric vehicles integration
	Smart home interactions
	Solar and wind energy generation
	Substation automation
	Energy storage
	Management of distributed energy resources (DER)

captures some of the examples presented of Smart Cities characterized by Smart Grids and outlines their key features related to the energy domain.

The recent and ongoing EU mission on Climate-Neutral and Smart Cities [71] is also supporting cities in accelerating their green and digital transformation, aiming at delivering 100 Climate-Neutral and Smart Cities by 2030 and ensuring those cities act as experimentation and innovation, paving the way to enable all European cities to engage in similar developments until 2050. In a recent publication [72], 14 showcases are identified where concepts such as Positive Clean Energy Districts (PCED), digital twins, and energy-efficient buildings are being deployed.

4.3. On Indexes, Evaluation, and Performance Metrics

In this section, we explore the characteristics of indexes and metrics being used to assess Smart Cities to understand to what extent the dimension of Smart Grids is covered.

The authors of [11] analyzed 16 assessment frameworks (8 related to Smart Cities and 8 related to urban sustainability) covering a wide geographical area (Europe, North America, Asia), collecting between 24 and 190 indicators for each of the 16 frameworks, resulting in a total of 958 indicators. The paper highlights the following results.

In Smart City frameworks, indicators tied to social sustainability hold a significantly prominent position, making up over half of all measured factors. Economic sustainability, on the other hand, accounts for slightly under one-third of these indicators. Finally, Environmental sustainability is somewhat less emphasized, comprising just around 20% of the total measures.

From the urban sustainability framework study, the result shows a balanced coverage between environmental and social dimensions (43% and 47%), while indicators addressing economic sustainability represent a minor part (10%).

The authors remark that the variety of Smart City definitions creates difficulties in establishing city goals, with the same situation happening regarding the sustainability concept. It is also noted that the standard recommendation ITU-T L.1440, “Methodology for environmental impact assessment of information and communication technologies at city level”, offers a methodology for calculating the life cycle impacts of ICT. Thus, the interaction between Smart Cities and Smart Grids does not appear to be covered with specific indicators.

In what concerns indexes, the Global Cities Report [73] addresses 29 metrics across five dimensions (business activity, human capital, information exchange, cultural experience, and political engagement) but does not address specific energy aspects.

In [74], the Smart City index is introduced with the aim of evaluating the economic, technological, and social aspects of Smart Cities. The methodology groups cities into four categories, determined by the Human Development Index scores published by the United Nations. Within these categories, each city is assessed according to residents’ impressions of five major factors: (a) health and safety, (b) mobility, (c) activities, (d) opportunities, and (e) governance. Again, the index does not address specific energy aspects.

The authors of [17] identify the metrics most common in the literature, which include Digital Readiness, Civic Engagement, Government Performance, Economic Performance, Sustainability, and Quality of Life.

In [70], the authors analyze seven different city rankings. They conclude that cities can appear in vastly different positions depending on the goals motivating each ranking system, as well as the particular indicators and methods employed. Furthermore, many of these assessments overlook or only partially include medium-sized urban centers, making existing rankings and benchmarks less applicable to their contexts. The authors then propose a new index for medium-sized cities based on six dimensions of “smartness” covering economy, people, governance, mobility, environment, and living.

From the examples analyzed, one can conclude that the assessment of Smart Cities’ performance and characteristics does not normally cover the dimension of the Smart Grid. Also, aspects related to costs, life cycle, size, or geography are seldom considered. A systemic approach toward implemented solutions, best practices, or roadmaps also appears to be limited.

To measure success rates of adopting Smart Grid technologies, one needs to take into consideration what success is according to the Smart City objectives. In this sense, the following are examples of metrics of success in this context that are easily measurable:

• Increased integration of renewable energy sources in the city (e.g., capacity or energy);
• Increase in infrastructure or energy for electric vehicles;
• Reduction in CO₂ emissions or improvement in air quality;
• Reliability of grids in the city (e.g., via the usual indicators SAIFI—system average interruption frequency index—or SAIDI—system average interruption duration index);
• Increase in the ratio of energy autonomy of the city (energy consumption versus energy “imported”);
• Increase in the ratio of energy autonomy of buildings (energy consumption versus energy “imported”);
• Field operation and maintenance operations avoided due to automation;
• Evolution of energy costs for different consumers;
• Number of islanding operations avoiding local blackouts.

Depending on each city’s objectives, these exemplify indicators that would provide a measure of the success of adopting Smart Grid technologies.

5. Discussion

The work allowed us to identify several characteristics in the literature related to the intersection between Smart Cities and Smart Grids. Sixteen (16) components were identified. In the following section, we will analyze the contribution of the sixteen components to the energy trilemma, as one of the major challenges associated with the deployment of Smart Grids and Smart Cities (in its energy component) derives from the push toward an environmentally sustainable energy system and the increasing introduction of renewable energy sources. Furthermore, at a level closer to implementation, we analyze the different components from a layered structure point of view in line with the Smart Grid Architecture Model (SGAM) [75,76]. Also, we propose a view of those sixteen components through a mapping against the Smart City characteristics and functionality identified before. After these views from multiple angles, we finalize with a discussion on areas that appear to have received less attention and propose topics that could deserve reinforced research and activity, which are laid out in the form of “research gap analysis”.

5.1. Addressing the Energy Trilemma

As mentioned at the outset of this work, we are interested in understanding how the corresponding Smart Grid components in a Smart City are contributing to addressing the challenges of the energy trilemma [5].

Table 2. Smart Grid components addressing the Smart City trilemma challenges.

		Energy Trilemma Dimension (Also in Smart Cities)
		Energy Security	Energy Equity	Environmental Sustainability	Observations
Smart Grid components relevant for Smart Cities	Advanced Metering Infrastructure (AMI)	x	x		Ensuring near real-time information, enhanced grid management and consumer information, energy efficiency
	Information and Communication Technology (ICT) infrastructure	x			Enhancing monitoring and control
	Grid modernization and sensor deployment	x	x		Enhancing monitoring and control, asset management, improving consumer engagement
	Internet of Things (IoT)	x	x		Enhancing monitoring and control, asset management, improving consumer engagement
	Renewable energy integration			x	Promoting reduction in Greenhouse Gas Emissions, and decreased air pollution
	Distributed Energy Resources (DERs)	x	x	x	Promoting decentralization of risk, remote energy access, and reducing emissions and air pollution
	Integration with energy storage systems	x			Ensuring energy system resilience
	Grid automation and self-healing	x			Ensuring energy system resilience and reliability
	Grid monitoring, data analytics, and artificial intelligence	x	x	x	Ensuring near real-time information, enhanced grid management and consumer information, energy efficiency
	Resilience enhancement	x			Ensuring energy system resilience
	Electric Vehicles (EV) and charging infrastructure			x	Promoting reduction in Greenhouse Gas Emissions, and decreased air pollution
	Integration with smart buildings and homes	x	x		Ensuring energy system resilience and reliability, optimization of costs for consumers
	Digital Twin	x			Ensuring near real-time information, enhanced grid management, and planning
	Microgrids	x	x		Allows energy system resilience and reliability, optimization of grid costs and losses
	Peer-to-peer (e.g., Blockchain) technologies		x		Allows optimization of energy costs
	Cybersecurity protection	x			Ensuring energy system resilience

provides that view, where for each of the components identified in Section 2, we map it to the energy trilemma aspect that it mostly addresses (with the symbol ‘x’). In the “Observations” column is indicated how the Smart Grid component contributes to the energy trilemma dimensions indicated.

In the context of the deployment of Smart Grids in Smart Cities, a central element is an environmentally sustainable energy system (one of the trilemma challenges) and an increasing introduction of renewable energy sources. The associated challenges then lead to the introduction of technological solutions to ensure energy security (another trilemma challenge) at different levels of the energy network and systems and at different timescales from operation to planning. Finally, the solutions need to identify the appropriate economic rationality and competitiveness to ensure affordable energy costs and prices (a final trilemma challenge). Table 2 reflects these dynamics, with a strong focus on technological solutions for ensuring energy security. The number of Smart Grid components for each of the trilemma challenges, however, does not represent the level of attention or focus because the components presented do not have the same level of abstraction but allow us to understand how each fits the existing challenges.

5.2. Layered Structure of a Smart Grid and a Smart City

As identified in the selected cases of developments and deployments discussed in the previous chapter, the implementation of the different components identified in our work covers distinct layers of a Smart Grid or a Smart City model. In this section, we explore this dimension.

Multiple structures and operational standardization approaches have been attempted for Smart Grid technologies. The Smart Grid Architecture Model, or SGAM, developed by CEN (European Committee for Standardization), CENELEC (European Committee for Electrotechnical Standardization), and ETSI (European Telecommunication Standards Institute), as a response to the mandate M/490 [77] from the European Commission, has received particular attention. The SGAM is a three-dimensional architectural framework that consists of five domains (generation, transmission, distribution, distributed energy resources, and customer premise) in one axe, six zones (process, field, station, operation, enterprise, and market) in another axe, and five interoperability layers (component, communication, information, function, and business) in a final axe.

In a similar approach, we look into the mapping of the components discussed above into the following layers: (i) ICT (Information and Communications Technology), associated with informatic systems and data collection mechanisms and infrastructure, (ii) Power System, related to the energy generation, transmission, distributions, and usage, (iii) Markets, encompassing the commercial roles, relationships, and flows, and (iv) Business, associated with the business models in place and justifying the transactions. The justification for adopting this approach is the reduction in complexity compared to SGAM while keeping key aspects for the understanding of the interaction between Smart Cities and Smart Grids, capturing fundamental aspects of the energy transition and the energy trilemma such as the digitalization aspect (ICT), the economic viability (markets and business) and the sustainable energy system (power system).

Table 3. Smart Grid components associated with different Smart Grid and Smart City layers.

		ICT	Power System	Markets	Business	Observations
Smart Grid components relevant for Smart Cities	Advanced Metering Infrastructure (AMI)	x				Data collection
	Information and Communication Technology (ICT) infrastructure	x				Data collection and transfer
	Grid monitoring and sensor deployment	x	x			Power system support and data collection
	Internet of Things (IoT)	x				Data collection and transfer
	Renewable energy integration		x			Power system deployment
	Distributed Energy Resources (DERs)		x	x	x	Encompasses power system deployment, market agents coordination, and business model
	Integration with energy storage systems		x	x	x	Encompasses power system deployment, market agents coordination, and business model
	Grid automation and self-healing		x			Power system deployment and management
	Resilience enhancement		x			Power system deployment and management
	Electric Vehicles (EV) and charging infrastructure		x	x	x	Encompasses power system deployment, market agents coordination, and business model
	Integration with smart buildings and homes	x	x	x	x	Encompasses all layers with a focus on power system
	Digital Twin	x	x			Power system support and data collection
	Microgrids		x			Power system deployment
	Peer-to-peer (e.g., Blockchain) technologies	x		x	x	Data flow management, market operators coordination, and business model
	Data analytics and artificial intelligence	x	x		x	Includes data collection and transfer, power system support, and business model management

presents the proposed mapping (with ‘x’ indicating an association of the component with a layer and a ‘x’ in bold format indicating a stronger such association), with the “Observations” column indicating how the Smart Grid component contributes to the layer indicated.

The different components addressed in this work cover the key levels of the architecture needed for the deployment and a Smart Grid in the context of a Smart City, with an expected focus on items related to the power grid. Chronologically, the ICT and power systems layers have received most of the original attention, with the need to monitor the energy system as its complexity increases and with the introduction of solutions in the grids and in system control (e.g., smart meters, AMI, grid automation, grid monitoring). Those are layers that still receive attention, although there is an increase in aspects associated with markets and business models and in the evolution from an electrical-only mindset to a cross-sector approach considering distinct energy vectors in a Smart Grid and, in particular, in a Smart City.

5.3. Smart Grid Components for Smart City Functionalities

As identified in the review work above, Smart City is a concept associated with multiple aspects, including energy. Focusing on the energy dimension, a Smart City is further associated with several different functionalities and characteristics, which characterize the narrative associated with Smart Cities in the energy context. In

Table 4. Smart Grid components and Smart City features and characteristics.

		Smart Grid Components Relevant for Smart Cities
		Advanced Metering Infrastructure (AMI)	ICT Infrastructure	Grid Modernization and Sensor Deployment	Internet of Things (IoT)	Renewable Energy Integration	Distributed Energy Resources (DERs)	Integration with Energy Storage Systems	Grid Automation and Self-Healing	Grid Monitoring, Data Analytics, and AI	Resilience Enhancement	EVs and Charging Infrastructure	Integration with Smart Buildings and Homes	Digital Twin	Microgrids	Peer-to-Peer Technologies	Cybersecurity Protection
Smart City features and characteristics	Management of high density of energy data points	x	x	x	x					x
	Management of linear, capilar (not meshed) energy system	x	x	x	x				x		x
	Benefiting from high density of smart buildings and smart homes		x	x	x		x	x					x		x	x
	Efficient ways to light and heat buildings		x	x	x			x	x				x
	More interactive and responsive city administration or utilities	x	x	x	x									x
	Urban heating and cooling						x	x					x		x
	Integration of Smart mobility and Smart urban transportation networks						x	x				x
	Smart energy system integration and Optimization of energy use across sectors (sector coupling)					x	x	x		x		x	x
	Collective management of distributed energy resources (DERs)		x		x	x	x	x		x			x		x
	Energy efficiency						x	x		x			x
	Increased connectivity and digital infrastructure	x	x	x	x
	Sustainable energy					x	x					x
	Resources efficiency		x	x	x		x			x
	Data-based decisions	x	x	x	x					x				x
	Local and cascaded energy management (hierarchy)		x											x	x	x

, we provide a linkage between the distinct Smart Grid components and the Smart City features and characteristics captured in the literature. These features and characteristics are aspects of the Smart Cities that are relevant for and have implications on Smart Grid deployment. It should be noted that these Smart City features and characteristics are not exhaustive or mutually exclusive. Some of the items convey similar aspects, although not coincidental.

Table 5. Description of Smart City features and characteristics.

		Description
Smart City features and characteristics	Management of high density of energy data points	Characteristic of city environments is the high number of energy delivery and generation points in a confined area
	Increased demand	Increased population moving into urban areas leading to increased energy and electricity consumption
	Management of increasing number of prosumers	With the technological and economical developments associated with distributed energy resources (namely solar rooftops and small-scale batteries), several city locations have also the ability to generate energy
	Bi-directional energy flows	With the increase in energy/electricity generation capabilities from the consumers side (prosumers), bi-directional flows occur in the grids posing technological challenges
	Management of linear, capilar (not meshed) energy system	Characteristic of city environments is the high number of linear feeders, non-mashed, high prone to service loss
	Benefiting from high density of smart buildings and smart homes	Automated (high volume of) buildings to balance the local energy system, engaging (high volume of) residents, commerce, industries through demand response—aggregation effect; buildings as heating storage
	Efficient ways to light and heat buildings	Potential high impact of energy efficiency (per km2) and grid support
	More interactive and responsive city administration or utilities in general	Increased resilience of energy system
	Urban heating and cooling	Flexibility and energy efficiency through integration of electrical and thermal energy systems
	Integration of Smart mobility and Smart urban transportation networks	Adoption of less air-polluting mobility
	Increased electrification	Given the efficiency characteristics of electricity, several energy loads are converting to electrification
	Smart energy system integration and Optimization of energy use across sectors (sector coupling)	Presence of different energy vectors in a geographically limited region
	Collective management of distributed energy resources (DERs)	Efficient handling of distinct distributed energy resources such as renewable energy sources, storage, multi-energy loads, EVs
	Energy efficiency	Efficient combination of energy generation, storage, and use
	Increased connectivity and digital infrastructure	High level of digitalization of connected devices and deployed technologies gathering data for analysis to identify optimization potential within the area, maximizing viability, resource efficiency, and adaptability
	Sustainable energy	Smart Cities/regions concentrate on energy generation through renewable resources like solar or wind to promote the usage of carbon-neutral energy
	Resources efficiency	By connecting DERs and assets through digital technologies, consumers can optimize energy usage based on real-time data
	Data-based decisions	Collected data allow improvements, curtailment, and efficiency via energy management system on household as well as city level
	Emerging market and business	Given the aspects mentioned above, specific market models emerge (e.g., peer-to-peer market mechanisms), and new business cases are developed (e.g., monetization of energy assets, management of mobility)
	Legal and regulatory implications	Given the aspects mentioned above, and in particular the Market and Business aspects, new legal and regulatory challenges emerge (e.g., roles and responsibilities, tariffs, grid connection requirements)
	Local and cascaded energy management (hierarchy)	Energy management systems from the local level to regional and central levels

sheds light on the aspects being considered by presenting a brief description of the functionalities and characteristics indicated. The links identified in the table should be read as capturing the “key focus” of a (Smart Grid) component toward a (Smart City) functionality or characteristic, and not as “contributing” (considering “contribution” would lead to much higher links and associations, thus reducing the ability to read and extract insight).

Table 4 provides insight into how the different functionalities and characteristics of a Smart City are being implemented based on Smart Grid components.

5.4. Gap Analysis

The review process associated with the identification of the sixteen Smart Grid components, the selected development and deployment cases of Smart Cities, and the evaluation methods and metrics identified surfaced several shortcomings in several different aspects. Furthermore, the mapping of the Smart Grid components associated with Smart Cities deployment against the energy trilemma challenges, against a layered implementation approach, and against Smart City characteristics and functionalities also highlights patterns and non-uniform coverage of several dimensions. As a result, we structure these shortcomings and patterns in the form of a gap analysis. We propose 11 research gaps to capture the areas and dimensions that require further work and understanding for effective and efficient deployment of Smart Grids in the context of Smart Cities.

i.

Successes versus failures

From our analysis, it could be noted that much of the research emphasizes the advantages associated with Smart Cities, whereas fewer works address potential shortcomings of these technologies or scrutinize unsuccessful projects. Indeed, the approaches and views are always from a very positive stance, as also identified by [29]. Section 4.3 on indexes, evaluation, and performance evidences this point. Knowledge about less successful cases would be important to understand which paths should not be pursued and to inform potential roadmaps, public policies, and private investments. Indeed, the causes for such cases can be technological, regulatory, policy-related, financial, and related to the characteristics of the corresponding city, among others.

ii.

Evaluation of results and outcomes

The terms used by the research community, such as “smart”, “intelligent”, and “digital”, among others, often imply a positive and unquestioning attitude regarding urban development. There seem to be many unstated assumptions and a somewhat self-praising inclination. Indeed, as mentioned in [44], cities want to be seen as smart, intelligent, creative, and cultural. The superficial use of the term in a political context is also mentioned in [78].

This runs the risk of studies regarding Smart Cities and Smart Grids, resulting in a techno-optimistic vision of the city’s digital modernization and its ambitions to become a “Smart City”. In this sense, it is noted that almost no research addresses the evaluation of whether existing pilots/examples of Smart Cities are delivering on their original promises. Again, the section above on indexes, evaluation, and performance confirms that, in general, results are not compared to initial objectives.

iii.

Cost–Benefit Analysis

Limited research has been found on the quantification of benefits, as noted in [31]. Even fewer cases in the literature address the topic of evaluating the costs of the different solutions proposed. Evaluations from a cost–benefit point of view should be more encouraged and are missing in the literature. The authors of [17] also noted that additional research is needed on strategies to overcome the challenges associated with Smart City implementations, along with methods for evaluating and contrasting the costs and benefits of such initiatives.

One exception to this situation is presented in [7], where it is considered that smart energy and electricity networks are key components in Smart City architectures and that integrating them into Smart City environments in a coherent and coordinated way requires a thorough investigation of their environmental, energy, economic, and social effects, supported by a cost–benefit analysis (CBA). The paper presents CBA findings for an Italian case based on specific parameters such as a defined social discount rate, carbon pricing, baseline emissions, pollutant costs, and a projected 20% increase in renewable energy generation over a ten-year period.

iv.

Life Cycle Analysis

The same concern as above can be mentioned related to life cycle analysis. Indeed, although it is an increasing concern in several political contexts, such analysis is not present in a clear manner in the literature regarding Smart Grid solutions for Smart Cities. As mentioned in [43], optimizing costs throughout the life cycle of a Smart City is still an activity that does not motivate research or reported practices.

Again, understanding the dynamics of costs, investments, and benefits over time would allow a more effective and efficient planning of deployments and investments, both from public and private entities.

v.

Solving technical vs. societal issues

Overall, it could also be noticed that existing research on Smart Cities has a stronger emphasis on either conceptual issues or underlying technical aspects and less on the implementation of Smart Cities and effective possible contributions of Smart Cities to solving societal issues. This is also remarked by [67].

vi.

Need for systemic assessment of best practices

The research on this topic would benefit from a more systematized approach toward capturing and making available tangible results from pilots and real implementations. This could require a set of parameters, indicators, and conditions, among other elements, to allow an understanding of the results obtained.

In the area of resilience, for example, several Smart Grid projects have addressed specific aspects of managing extreme events, but cohesive, comprehensive, general-purpose solutions are not clear. This is something that could stimulate quicker adoption and benefit the industry and related stakeholders.

As can be seen in Section 4.2 on case studies and best practices, there is a lack of analysis on what works in particular contexts, namely geographies, continents, city characteristics, and level of development, among other dimensions.

vii.

Citizens’ privacy, security, and access to benefits

The growing deployment of energy sensors and automated control systems in both public infrastructures and residential settings brings forth concerns regarding privacy, as well as the capacity to oversee and manage urban mobility and energy flows. This can become a barrier to the smooth development of Smart Grids in Smart Cities; therefore, the topic should be addressed more clearly.

Also, along with this rationale, the reliance of Smart Grids and Smart Cities on high-speed internet connections may create asymmetries in the quality of energy access and result in emerging forms of urban fragmentation, digital divide, and so-called “energy poverty”. To avoid it becoming a future barrier, it should be addressed from the outset.

viii.

Business models and market solutions

Although most of the technical challenges can be addressed using current and anticipated technologies, the real obstacles involve the institutional, market, and social frameworks that must support the rollout of Smart Grids. Technologies have the capability to drive transformations, but if Smart Grids (in particular in the context of Smart Cities) are to become a genuinely socio-technical–economic reality, more needs to be done. This includes establishing equitable retail electricity markets, creating local/community energy marketplaces capable of handling high volumes of possible automated prosumer transactions, ensuring the secure handling of consumers’ commercial information, and actively involving end users in energy markets (also highlighted in [7]). Also mentioned by the same authors [3], it is important to research aspects related to the prosumer role, namely market design, the relation between provider and consumer, and strategies for consumer participation.

ix.

Cross-sector

As highlighted in [29], some approaches to Smart Grid involve the integration of multiple sectors such as water, gas, heating, cooling, waste management, and electric mobility. Approaches aiming at an appropriate intersection between Smart Grids and Smart Cities should increasingly address these multi-energy systems (as also referred to in [4]) to ensure collective management of distributed energy resources and optimization of energy use across sectors via a digital infrastructure and make use of data-based decisions.

x.

Multiple angles of analysis

Some further aspects that are not clearly identified in the literature need to be analyzed along several relevant axes. It would be important to structure the analysis in multiple vectors of analysis such as the following:

• Economic vs. social vs. environmental;
• Top-down vs. bottom-up;
• Exploration of geographical differences;
• Size of Smart Cities;
• Solutions for residential vs. commercial vs. industry vs. services;
• Different “actors”/stakeholders point of view.

As discussed in Section 4.2, the size of cities may imply different challenges and implementation options. To highlight another vector of analysis (economic) identified above, it is worth mentioning that cities in low-income countries face a feasibility challenge regarding the implementation of Smart Grids. The high initial costs can be prohibitive, the tax base has fewer resources, the existing infrastructure is normally weaker, and the human capital is limited. In these situations, making use of modular, scalable solutions, identifying phased and segmented approaches, and making use of public–private partnerships is key to making the development feasible and ensuring that societies do not lag behind in aspects they consider relevant for their progress.

To support this multiplicity of angles of analysis, it would be helpful to have a proposal for a taxonomy for analysis, which is currently very ad hoc and fragmented.

xi.

Roadmap approach

Finally, from a practical implementation point of view and as a contribution to practitioners, authorities, investors, and other stakeholders, it would be useful to have an analysis of the stepwise approaches and improvements toward a Smart City in the context of Smart Grids that would make sense for a different type of situations, namely taking into consideration the vectors mentioned above. Again, as can be seen in Section 4.2 on Smart Cities deployments, there is a lack of analysis on how the development and deployment of Smart Grids in Smart Cities should be under distinct contexts, such as geographies, continents, city characteristics, and level of development, among other dimensions. This could provide a possible set of roadmaps of implementation that could help follow successful paths, leading to increasing societal welfare and corresponding acceptance.

Table 6. Gaps identified in work related to Smart Cities in the context of Smart Grids.

Gaps Identified	Explanation
Successes versus failures	Current literature focuses on successes; case of failures analysis almost absent in the literature
Evaluation of results and outcomes	Metrics and results not compared to initial objectives; few cases in the literature but not in a systematic manner
Cost–benefit analysis	Few examples considering costs and trade-offs
Life cycle analysis	Cases with analysis on longer term view or circular economy almost absent in the literature
Technical vs. societal issues	Very few cases of analysis on citizens’ impact and well-being
Systemic assessment of best practices	Few cases with common indicators and systematic approaches; lack of analysis according to multiple cities’ characteristics
Citizens’ privacy, security, and access to benefits	Few cases addressing citizen concerns and “energy poverty” risks
Business models and market solutions	Cases moving from technological view to market and business implications almost absent in the literature
Cross-sector	Few examples addressing the benefits and challenges of considering multiple energy vectors in the city
Multiple angles of analysis	Exploration of geographical differences, city characteristics, different stakeholders’ views, etc., almost absent in the literature
Roadmap approach	Roadmap proposals according to city characteristics not found in the literature

captures the gaps identified above.

The information above in Table 6 provides information on which gaps are more notorious in the literature reviewed. Identifying which ones are more “important” in the sense of contributing to effective, efficient, and valuable implementation is, to a large extent, a subjective exercise, even considering formal methodologies such as TOPSIS or AHP. In Figure 5, we present such a qualitative attempt by also including a view on the dimension of “difficulty of implementation”, being from the access to data or evaluation aspects, among others.

6. Conclusions

The paper aims to contribute to the understanding of the current situation of the development of Smart Cities in one of its many aspects, which is the integration into a wider Smart Grid. Simultaneously, it addresses how the Smart Grid is faring in the development of one of its components, which is bringing the Smart Grid up to the cities.

The research thus lays the understanding of a Smart City, also addressing the close concept of a Sustainable City and of the Smart Grid. It proceeds by identifying the points of contact between the two concepts. The analysis concluded that there exists, as expected, an extensive material of research associated with this intersection area, although it is not always very easy to have a clear cut that would facilitate the analysis by researchers and practitioners.

The main elements that bring the concept of Smart Grids into the Smart City were reviewed, and the literature was referenced regarding the state-of-the-art and current trends and research developments. In this context, we identify 16 components in the intersection space between Smart Cities and Smart Grids, which are key for effective and efficient deployment.

Furthermore, existing implementations and experience from the field were also reviewed, referring to the publications where case studies, pilots, rankings, evaluations, and assessments are available. The review concluded that the assessment of Smart Cities performance and characteristics does not normally cover the dimension of Smart Grid and that aspects related to costs, life cycle, size, or geography are seldom considered. A systemic approach toward implemented solutions, best practices, or roadmaps also appears to be limited.

We further propose a mapping of the 16 components identified against the energy trilemma challenges to further highlight their relevance for the corresponding challenges. This allowed us to have an understanding of which components address energy security, energy equity, and sustainability and evidenced the dynamics from sustainability to energy security and finally to energy equity and competitiveness. The mapping contributes to guiding, communicating, and evaluating Smart Grid deployments in Smart Cities for different involved parties, including policy and community stakeholders.

The deployment of Smart Grids in Smart Cities benefits from a structured analysis of its components, and models are insightful in this sense. Departing from the Smart Grid Architecture Model (SGAM), we identified four key layers at which deployment of Smart Grids in Smart Cities occurs, namely an ICT layer, a power systems layer, a markets layer, and a business layer. By mapping the 16 components into these layers, we concluded that the ICT and power systems layers have received most of the original attention in the literature and deployments, as the need to monitor the energy system as its complexity increases and with the introduction of solutions in the grids and in system control (e.g., smart meters, AMI, grid automation, grid monitoring). Although those layers still receive attention, we noticed new dynamics with the emergence of aspects associated with markets and business models. Additionally, both the literature and the mapping show a gradual evolution from an electrical-only mindset to a cross-sector approach considering distinct energy vectors in Smart Grids for Smart Cities. Again, the mapping performed is helpful in guiding and understanding concrete implementations by understanding how the different layers are covered.

As identified in the literature review performed, Smart Cities evidence a set of characteristics and features. These include the high density of energy data points, the efficient lighting and heating of buildings, the collective management of DERs, and the integration and optimization of energy use among sectors (sector coupling), among others. The mapping of the 16 components into that set of characteristics and features allows us to detect which components have a key focus on specific aspects of the Smart City. This can guide different stakeholders and practitioners and their processes of planning, deploying, and developing Smart Grids in Smart Cities. Additionally, it helps shape the solutions to implement under specific characteristics of the city or geography.

Finally, a critical review of the literature analyzed was performed, identifying areas that seem to have received less attention and proposing topics that could deserve reinforced research and activity. Here, our work contributes to 11 research gaps identified. Indeed, the existing work focuses almost exclusively on success cases and lacks insight into lessons learned from unsuccessful pilots or deployments. In relation to this, a systemic assessment of best practices is not evident in the literature. Also, the comparison of metrics and results with initial objectives is almost absent in the literature. Aspects related to cost–benefit analysis and life cycle analysis receive limited attention when Smart Grids deployments in Smart Cities are discussed and analyzed. It was also noted that most studies focus on technological aspects rather than market and business implications or societal impact. Another aspect that deserves additional attention is the exploration of different angles of analysis and structuring the analysis in multiple vectors, such as the size of cities, geographical characteristics, profile in terms of residential, commercial, and industrial loads, and different stakeholders’ viewpoints, among others. This would be a contribution to practitioners, authorities, investors, and other stakeholders to identify stepwise approaches and improvements toward a Smart City in the context of Smart Grids that would make sense for different types of situations and under distinct contexts, such as geographies, continents, city characteristics, and level of development, among other dimensions. These gaps can be addressed via the following research and policy recommendations and measures:

• Research activities or reviews dedicated to capturing best practices in a systematic manner;
• Research activities or reviews dedicated to capturing aspects from pilots, deployments, or experiences in cities that could not achieve the expected results, with identification of lessons learned;
• Setting of objectives and key performance indicators at the beginning of pilots and deployment (by relevant decision makers) in cities and consequent research on the level of achievements;
• Research activities or reviews dedicated to cost–benefit analysis and life cycle assessments of projects and deployments in cities, following pre-defined methodologies identified by policy makers;
• Use of the structures proposed in this paper distinguishing domains in the energy trilemma and in the layered Smart Grids Models to evidence and highlight research being performed in different domains or at different layers;
• Research activities and reviews dedicated to capturing in a systematic way the implications of different city characteristics (e.g., size, geography, load and generation profiles, wealth) and the results as seen from different stakeholders’ points of view.

This will contribute to more robust decision-making, roadmap identification, and more effective and efficient deployment of Smart Grids in a Smart City context by the multiple stakeholders involved, from policy makers to public entities to industry players and to citizens.